Academic Journal
A multi-scale convolutional and color-adaptive approach for sensory enhancement in cultural and creative product packaging.
| Title: | A multi-scale convolutional and color-adaptive approach for sensory enhancement in cultural and creative product packaging. |
|---|---|
| Authors: | Xu, Junyi, Liu, Linian |
| Source: | PeerJ Computer Science; Sep2025, p1-22, 22p |
| Subject Terms: | Packaging design, Image enhancement (Imaging systems), Convolutional neural networks, Image processing, Aesthetic experience, Chromaticity, Aesthetics of art |
| Abstract: | This study addresses the critical need for enhanced visual appeal in cultural product packaging by proposing a novel multi-scale convolutional neural network (MCCNN) with adaptive color enhancement. Unlike existing methods that struggle with uneven lighting and detail loss, our approach innovatively combines laser-based 3D feature fusion with illumination-aware enhancement to overcome these limitations. The method extracts multi-level visual features from packaging images through scale transformation and feature fusion, constructing a laser-based 3D multi-scale feature fusion model to achieve image preprocessing and noise reduction. Furthermore, by employing block matching and fuzziness detection techniques, a visual constraint model is established to effectively extract features from blurred regions and detect image block information. In terms of image enhancement, the integration of illumination compensation and adaptive dehazing techniques addresses issues such as image fogging and detail loss during brightness adjustment, thereby improving image quality and color richness. Experimental results demonstrate that the proposed method achieves a 90.62% completeness rate in 3D reconstruction of product packaging images, with an average design time of less than 5.3 s. Additionally, the color enhancement module shows outstanding performance, with a color enhancement effect of 94.99%, an image fitness value of 1.0148, and an information entropy of 78.96%, effectively enhancing image contrast and visual quality. This research offers new insights and technical support for the intelligent sensory design of cultural and creative product packaging. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=23765992&ISBN=&volume=&issue=&date=20250901&spage=1&pages=1-22&title=PeerJ Computer Science&atitle=A%20multi-scale%20convolutional%20and%20color-adaptive%20approach%20for%20sensory%20enhancement%20in%20cultural%20and%20creative%20product%20packaging.&aulast=Xu%2C%20Junyi&id=DOI:10.7717/peerj-cs.3230 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A multi-scale convolutional and color-adaptive approach for sensory enhancement in cultural and creative product packaging. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+Junyi%22">Xu, Junyi</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Linian%22">Liu, Linian</searchLink> – Name: TitleSource Label: Source Group: Src Data: PeerJ Computer Science; Sep2025, p1-22, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Packaging+design%22">Packaging design</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Aesthetic+experience%22">Aesthetic experience</searchLink><br /><searchLink fieldCode="DE" term="%22Chromaticity%22">Chromaticity</searchLink><br /><searchLink fieldCode="DE" term="%22Aesthetics+of+art%22">Aesthetics of art</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study addresses the critical need for enhanced visual appeal in cultural product packaging by proposing a novel multi-scale convolutional neural network (MCCNN) with adaptive color enhancement. Unlike existing methods that struggle with uneven lighting and detail loss, our approach innovatively combines laser-based 3D feature fusion with illumination-aware enhancement to overcome these limitations. The method extracts multi-level visual features from packaging images through scale transformation and feature fusion, constructing a laser-based 3D multi-scale feature fusion model to achieve image preprocessing and noise reduction. Furthermore, by employing block matching and fuzziness detection techniques, a visual constraint model is established to effectively extract features from blurred regions and detect image block information. In terms of image enhancement, the integration of illumination compensation and adaptive dehazing techniques addresses issues such as image fogging and detail loss during brightness adjustment, thereby improving image quality and color richness. Experimental results demonstrate that the proposed method achieves a 90.62% completeness rate in 3D reconstruction of product packaging images, with an average design time of less than 5.3 s. Additionally, the color enhancement module shows outstanding performance, with a color enhancement effect of 94.99%, an image fitness value of 1.0148, and an information entropy of 78.96%, effectively enhancing image contrast and visual quality. This research offers new insights and technical support for the intelligent sensory design of cultural and creative product packaging. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PeerJ Computer Science is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7717/peerj-cs.3230 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Packaging design Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Image processing Type: general – SubjectFull: Aesthetic experience Type: general – SubjectFull: Chromaticity Type: general – SubjectFull: Aesthetics of art Type: general Titles: – TitleFull: A multi-scale convolutional and color-adaptive approach for sensory enhancement in cultural and creative product packaging. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, Junyi – PersonEntity: Name: NameFull: Liu, Linian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 23765992 Titles: – TitleFull: PeerJ Computer Science Type: main |
| ResultId | 1 |